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- Title
Comparing optimization methods for deep learning in image processing applications.
- Authors
Geng, Alexander; Moghiseh, Ali; Redenbach, Claudia; Schladitz, Katja
- Abstract
Training a deep learning network requires choosing its weights such that the output minimizes a given loss function. In practice, stochastic gradient descent is frequently used for solving the optimization problem. Several variants of this approach have been suggested in the literature. We study the impact of the choice of the optimization method on the outcome of the learning process at the example of two image processing applications from quite different fields. The first one is artistic style transfer, where the content of one image is combined with the style of another one. The second application is a real world classification task from industry, namely detecting defects in images of air filters. In both cases, clear differences between the results of the individual optimization methods are observed.
- Subjects
DEEP learning; IMAGE processing; PROBLEM solving; ARTISTIC style; CONVOLUTIONAL neural networks
- Publication
Technisches Messen, 2021, Vol 88, Issue 7/8, p443
- ISSN
0171-8096
- Publication type
Article
- DOI
10.1515/teme-2021-0023